@InProceedings{he-EtAl:2017:Long3,
author = {He, Luheng and Lee, Kenton and Lewis, Mike and Zettlemoyer, Luke},
title = {Deep Semantic Role Labeling: What Works and What’s Next},
booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
month = {July},
year = {2017},
address = {Vancouver, Canada},
publisher = {Association for Computational Linguistics},
pages = {473--483},
abstract = {We introduce a new deep learning model for semantic role labeling (SRL) that
significantly improves the state of the art, along with detailed analyses to
reveal its strengths and limitations. We use a deep highway BiLSTM architecture
with constrained decoding, while observing a number of recent best practices
for initialization and regularization. Our 8-layer ensemble model achieves 83.2
F1 on theCoNLL 2005 test set and 83.4 F1 on CoNLL 2012, roughly a 10% relative
error reduction over the previous state of the art. Extensive empirical
analysis of these gains show that (1) deep models excel at recovering
long-distance dependencies but can still make surprisingly obvious errors, and
(2) that there is still room for syntactic parsers to improve these results.},
url = {http://aclweb.org/anthology/P17-1044}
}